Computational and Mathematical Methods in Medicine

Research Article

Feature Selection Method Based on Partial Least Squares and Analysis of Traditional Chinese Medicine Data

LAPLS

	Input: Dataset D
	Output: LAPLS regression equation
	Begin:
	(1) Standardize the dataset D to get ;
	i = 1
	(2) While (the number of latent variables i has yet to reach satisfactory accuracy)
	(a) Calculate the maximum eigenvalue of the matrix and its corresponding eigenvector ;
(b) Calculate the latent variables score vectors and based on the feature vector ;
(c) Calculate the load vector and the square of : , , and the residual information matrix and ;
	End
	(3) Solve the multiple regression equation and denormalize the regression coefficient: ;
	(4) Construct the objective function in conjunction with the L1 regular term constraint: ;
	(5) Use the coordinate descent method to iterate multiple times, solve the compressed regression coefficient , and construct a new regression equation ;
	End